Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv Machine Learning

Jul 30

SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution

arXiv:2607. 26784v1 Announce Type: new Abstract: Large language model agents often encounter related yet distinct tasks that share reusable solution patterns.

By Zhiyuan Yao, Yuxin Chen, Zhengxi Lu, Zishan Xu, Yueqing Sun, Yifu Guo, Yuquan Lu, Zhengzhou Cai, Kangning Zhang, Zhuowen Han, Zi-Han Wang, Ziang Ye, Qi Gu, Xunliang Cai, Weiwen Liu, Yongliang Shen

llms agents reinforcement-learning

arXiv Machine Learning

Jul 30

ReCo: Reweighting GRPO Against Distributional Concentration

arXiv:2607. 26862v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has become a standard reinforcement learning method for post-training language models.

By Junoh Park, Junseo Hwang, Wonguk Cho, Taesup Kim

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jul 30

HealthSLM-Bench: Benchmarking Small Language Models for Mobile and Wearable Healthcare Monitoring

arXiv:2509. 07260v5 Announce Type: replace-cross Abstract: Mobile and wearable healthcare monitoring play a vital role in facilitating timely interventions, managing chronic health conditions, and ultimately improving individuals' quality of life.

By Xin Wang, Ting Dang, Xinyu Zhang, Vassilis Kostakos, Michael J. Witbrock, Hong Jia

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jul 30

REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage

arXiv:2604. 01527v4 Announce Type: replace-cross Abstract: Production deployment of AI coding agents requires fast, reproducible evaluation signals.

By Smriti Jha, Matteo Paltenghi, Chandra Maddila, Vijayaraghavan Murali, Shubham Ugare, Satish Chandra

llms agents benchmarks

arXiv Machine Learning

Jul 30

Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility

arXiv:2607. 26828v1 Announce Type: new Abstract: Large language models increasingly support scientific and algorithmic discovery through inference-time search over evaluated candidates.

By Yansen Zhang, Yilu Liu, Tianyu Liu, Jiamin Chen, Xiaokun Zhang, Kai Xie, Xue Liu, Chen Ma, Yiyan Qi

llms benchmarks

arXiv Machine Learning

Jul 30

Knowledge before Reasoning: EC-Reason-Bench, a Training-Free Diagnostic Benchmark for LLM Enzyme Classification

arXiv:2607. 26397v1 Announce Type: cross Abstract: Enzyme function prediction is a hierarchical, knowledge-intensive form of protein function classification.

By Linyu Li, Zhi Jin, Yichi Zhang, Dongming Jin, Yuanpeng He, Huanyao Zhang, Xuan Zhang, Gadeng Luosang, Nyima Tashi

llms benchmarks safety

arXiv Machine Learning

Jul 30

Incast-Free MoE Rate-Based Scheduling

arXiv:2607. 26340v1 Announce Type: cross Abstract: Mixture of Experts (MoE) architectures have become key to large language models; however, their typical round-robin (RR) scheduling introduces significant bottlenecks.

By Evyatar Cohen, Jose Yallouz, Alexander Shpiner, Mark Silberstein, Sylvia Ratnasamy, Isaac Keslassy

llms

arXiv Machine Learning

Jul 30

Sky sphere representation in language models

arXiv:2607. 27092v1 Announce Type: new Abstract: We analyze whether language models of size ~100B have a representation of the night sky map that is decodable from their residual stream.

By Aleksandr Berdnikov, Yevgeny Liokumovich

llms

arXiv Machine Learning

Jul 30

PowerAtlas: Towards Electricity-Computing Co-Scheduling for Power Systems

arXiv:2607. 26710v1 Announce Type: new Abstract: The rapid growth of AI workloads is turning data centers into large-scale, volatile, yet spatiotemporally flexible grid loads, creating an urgent need for coordinated electricity-computing scheduling.

By Kaiwen Jiang, Siya Xu, Ziyue Zhu, Chao Yang, Anh Tuan Luu, Haoran Luo

llms agents benchmarks

arXiv Machine Learning

Jul 30

Meta-Learned Reward Shaping for Reinforcement Learning from Human Feedback

arXiv:2607. 26094v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is the standard approach for aligning large language models with human preferences, but its quality is limited by static, task-agnostic reward models.

By Yunpeng Chu

llms reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 30

Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance

arXiv:2607. 26333v1 Announce Type: cross Abstract: Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment.

By Panagiotis Fytas, Ian Selby, Clemens Karner, Judith Babar, Simon Baker, Jake Beckford, Timothy J. Sadler, Shahab Shahipasand, Arthikkaa Thavakumar, John Li Chen, Alex Sawer, Michael Roberts, Jonathan Weir-McCall, J. H. F. Rudd, Carola-Bibiane Sch\"onlieb, Anna Korhonen, Anna Breger

llms fine-tuning multimodal safety

arXiv Machine Learning

Jul 30

AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents

arXiv:2607. 26998v1 Announce Type: cross Abstract: Large language model (LLM) agents automate penetration testing through an observation-action loop, selecting actions based on observations returned by tools.

By Ruoyu Wang, Heng Zhao, Renjie Wu, Mengnan Zhao, Zhixuan Chu, Wanyu Lin, Tianhang Zheng

llms agents

arXiv Machine Learning

Jul 30

MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent

arXiv:2507. 02259v2 Announce Type: replace-cross Abstract: Despite improvements by length extrapolation, efficient attention and memory modules, handling infinitely long documents with linear complexity without performance degradation during extrapolation remains the ultimate challenge in long-text processing.

By Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, Ya-Qin Zhang, Wei-Ying Ma, Jingjing Liu, Mingxuan Wang, Hao Zhou

llms agents reinforcement-learning

arXiv Machine Learning

Jul 30

Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmark

arXiv:2607. 26993v1 Announce Type: new Abstract: Face presentation attack detection (PAD) remains challenging under cross-dataset evaluation, where domain shift degrades models trained on a single dataset.

By Peter Lorenz, Anjith George, S\'ebastien Marcel

llms multimodal benchmarks

arXiv Machine Learning

Jul 30

Train Large, Deploy Compact: Structured Compression for Compact Low-Rank Adaptation

arXiv:2510. 00192v3 Announce Type: replace Abstract: Low-rank adaptation (LoRA) has become a widely used paradigm for parameter-efficient fine-tuning of large language models, yet its representational capacity often lags behind full fine-tuning.

By Xin Yu, Cong Xie, Xunmei Liu, Tiantian Fan, Lingzhou Xue, Zhi Zhang

llms fine-tuning efficiency

arXiv Machine Learning

Jul 30

MathNet: a Global Multimodal Benchmark for Mathematical Reasoning and Retrieval

arXiv:2604. 18584v2 Announce Type: replace-cross Abstract: Mathematical problem solving remains a challenging test of reasoning for large language and multimodal models, yet existing benchmarks are limited in size, language coverage, and task diversity.

By Shaden Alshammari, Kevin Wen, Abrar Zainal, Mark Hamilton, Navid Safaei, Sultan Albarakati, William T. Freeman, Antonio Torralba

llms rag multimodal benchmarks

arXiv Machine Learning

Jul 30

Learning Dynamic User Personas from Implicit Interaction Streams via Iterative Refinement

arXiv:2607. 26473v1 Announce Type: new Abstract: Personalizing large language models (LLMs) to individual users is essential for improving user experience, yet existing approaches typically rely on explicit preference supervision such as pairwise comparisons or demographic attributes, limiting their applicability in natural interaction settings.

By Haifeng Wu

llms agents robotics

arXiv Machine Learning

Jul 30

Two Calls Beat Five Agents: Evaluating Multi-Agent Pipelines Against Self-Refinement for Local Language Models

arXiv:2607. 26922v1 Announce Type: new Abstract: Multi-agent LLM pipeline systems break down the task among multiple roles for better reasoning, but are benchmarked mainly with large-scale commercial models.

By Ashish Prajapati, Om Mohite

llms agents benchmarks

arXiv Machine Learning

Jul 30

Harnessing Large Language Models for Intelligent Resource Allocation in the Internet of Everything

arXiv:2607. 26602v1 Announce Type: cross Abstract: The rapid development of the Internet of Everything (IoE) is accelerating the adoption of intelligent applications.

By Haijun Zhang, Zhuojun Duan, Zijun Wu, Xu Ma, Yuzheng Ren

llms

arXiv Machine Learning

Jul 30

Dense Local Dependencies Induce Attention-Logit Explosion and Training Instability During Long-Sequence Transformer Training

arXiv:2505. 15548v2 Announce Type: replace Abstract: Autoregressive transformer language models frequently exhibit training instability when trained on long sequences, particularly under low-precision arithmetic.

By Suvadeep Hajra

llms

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